Federated Learning (FL) was recently proposed [1]} [2]} as a new learning paradigm promoting the execution and specialization of models on devices, called clients. At one point, clients models are sent to a server where they are aggregated into a more generic one. This new model is redistributed to the clients for a new local learning iteration, and so on. Federated Learning reduces communication costs and improves security because only models, and no private data, are exchanged [3]}. It has immediately attracted attention in the pervasive field but it rapidly appeared that adaptations were needed to meet specific challenges of this domain [4]}. Originally, Federated Learning was set out as an alternative form of distributed learning to obtain a single well-performing model (on the server side) without ever communicating user data. However, to deal with pervasive computing, this learning approach should shift from a single server-centric model to a multi client-centric model objective [5]}. Precisely, client models should perform well on local data (strong personalization) as well as on data only seen by others (good generalization).
